Which of the following statements about the Attributional Retraining studies is FALSE

Sci Stud Read. Author manuscript; available in PMC 2020 Jun 10.

Published in final edited form as:

PMCID: PMC7286625

NIHMSID: NIHMS1594589

Kimberley C. Tsujimoto,a Richard Boada,b Stephanie Gottwald,c Dina Hill,d Lisa A. Jacobson,e,f Maureen Lovett,a,g E. Mark Mahone,e,f Erik Willcutt,h Maryanne Wolf,c Joan Bosson-Heenan,i Jeffrey R. Gruen,i and Jan C. Frijtersj

Abstract

The causes that individuals attribute to reading outcomes shape future behaviors, including engagement or persistence with learning tasks. Although previous reading motivation research has examined differences between typical and struggling readers, there may be unique dynamics related to varying levels of reading and attention skills. Using latent profile analysis, we found 4 groups informed by internal attributions to ability and effort. Reading skills, inattention, and hyperactivity/impulsivity were investigated as functional correlates of attribution profiles. Participants were 1,312 youth (8–15 years of age) of predominantly African American and Hispanic racial/ethnic heritage. More adaptive attribution profiles had greater reading performance and lower inattention. The reverse was found for the least adaptive profile with associations to greater reading and attention difficulties. Distinct attribution profiles also existed across similar-achieving groups. Understanding reading-related attributions may inform instructional efforts in reading. Promoting adaptive attributions may foster engagement with texts despite learning difficulties and, in turn, support reading achievement.

Children with reading and attention difficulties have a greater risk of academic failure and negative academic self-perceptions (e.g., Lee & Zentall, 2012; Linnenbrink & Pintrich, 2002; Margolis & MaCabe, 2006). Children who continuously struggle with school may begin to doubt their abilities and feel helpless (Butkowsky & Willows, 1980; Chan, 1994), which leads to less engagement with learning tasks (Guthrie & Davis, 2003; Paige, 2011). These psychological and behavioral consequences (Weiner, 2010) resulting from experiences with failure are characteristic of maladaptive attributions. In contrast, children who experience failure but have more adaptive attributions are less likely to experience similar negative consequences.

Causal attributions for reading success and failure have been explored for typical and struggling readers (e.g., Butkowsky & Willows, 1980; Frijters et al., 2018; Tsujimoto et al., 2018). However, reading difficulties often co-occur with attention deficit/hyperactivity disorder (ADHD), specifically the attentional component (Martinussen, Grimbos, & Ferrari, 2014; Massetti et al., 2008). Are motivation and/or attribution patterns observed with struggling readers unique to reading, or to related challenges such as inattention? Understanding contributions of co-occurring learning difficulties may help understand variation in achievement-motivation relationships.

Attributions for reading success and failure

As children experience success and failure, they hold a set of beliefs about the causes of their learning outcomes. Weiner’s (1985, 1986; see also Heider, 1958) attribution theory asserts that these beliefs vary on three dimensions: stability, locus of causality, and internal versus external control. Stability is associated with the likelihood that an attribute will change over time. Locus of causality is associated with whether a cause is internal or external to the individual. The third dimension is linked to the degree of control, either internally or externally, in influencing the cause of an event (Weiner, 2010). Traits, such as aptitudes, are less malleable compared to effort, which may vary from task to task, though the stability of some attributions is debated (Muenks & Miele, 2017). For instance, ability defined as intellectual capacity is likely to be considered more stable; however, remedial programs can result in growth (i.e., change) in ability over time.

In the context of reading, consider a child who received a respectable grade on a reading assignment: Does the child view this “success” as a result of being a good reader, studying hard, or good luck? Also consider a child who was unable to complete a reading assignment: Is this “failure” attributed to poor reading ability, little effort, or the fact that the assignment was too difficult and impossible to complete? In situations of success, attributions to more stable and internal causes such as ability are positive and promote engagement in anticipation of future success (Weiner, 1986, 1994), but the same stable and internal attributions in situations of failure are less adaptive. Causal consequences of attributions that influence behavior are partly driven by beliefs about future events. Expectancy value theory argues that engagement or persistence is associated with the perceived value of a task and the expected performance or payout (Atkinson, 1957; Wigfield & Guthrie, 2000). Suppose the child with the respectable reading grade was asked to perform another reading task. This child may expect to replicate good performance and be willing to engage in the task compared to the child who struggled and may be more likely to avoid the task to avoid a negative outcome. Attributions to less stable, but controllable, causes such as effort are most adaptive, especially in situations of failure (Perry & Hamm, 2017; Weiner, 1980). Individually, attribution constructs cannot easily be categorized as either adaptive or maladaptive. More important are the patterns of attributions across causal dimensions relative to personal learning experiences (Weiner, 2010).

Reading and attention: An attribution model assessing co-occurring deficits

The relationship between reading and attention has been repeatedly reported in the literature (e.g., Daley & Birchwood, 2010; Willcutt & Pennington, 2000; Willcutt et al., 2001) and demonstrated across the developmental span (e.g., Massetti et al., 2008; Willcutt et al., 2007) and with a range of reading skills (Arrington, Kulesz, Francis, Fletcher, & Barnes, 2014; Jacobson et al., 2011; Martinussen et al., 2014). This association extends to the clinical range for both skills, with the prevalence of ADHD among individuals identified with reading disabilities (RD) ranging from approximately 20% to 40% (Sexton, Gelhorn, Bell, & Classi, 2012; Willcutt et al., 2001). When impairments in both reading and attention domains co-occur, the overall risk for academic failure may be greater (Lee & Zentall, 2012). Individuals who struggle academically tend to be less interested or engaged with learning tasks (Guthrie & Davis, 2003; Paige, 2011) and can have negative academic self-concept and ability perceptions (see Linnenbrink & Pintrich, 2002; Margolis & MaCabe, 2006) compared to typically developing peers. However, Kistner, Osborne, and LeVerrier (1988) reported that not all struggling readers experienced negative attributions (also see Licht, Kistner, Ozkaragiz, Shapiro, & Clausen, 1985). In addition, Núñez et al. (2005) presented evidence of heterogeneity in attribution profiles among struggling learners, suggesting distinct patterns of both adaptive and maladaptive (i.e., helplessness) attributions exist among those with learning difficulties.

There are very few investigations of reading motivation, and more specifically attributions, among children with co-occurring reading and attention challenges. As with RD, children with attention difficulties are at risk for problems with achievement motivation with some reports of lower academic self-concept and self-efficacy and less adaptive attribution styles (Lee & Zentall, 2012; Tabassam & Grainger, 2002; Zentall & Beike, 2012). For instance, Hoza, Pelham, Waschbusch, Kipp, and Sarno Owens (2001) found that boys with ADHD were more likely than controls to attribute success to luck and less likely to attribute failure to controllable causes such as effort. Less persistence on a word-puzzle task was also found for those with ADHD. Moreover, Lee and Zentall (2012) found that students with reading and attention deficits experienced lower levels of intrinsic and extrinsic motivation, as well as greater work avoidance earlier than children with RD alone. Even subthreshold clinical levels of reading and attention difficulties may be problematic for promoting positive academic attributions and related behaviours that support academic achievement.

One complexity involved in sorting attributions in relation to developmental reading delays are developmental changes in attributions. As children age and acquire greater experiences of success and failure, attributions for why these events occur become more developed. Past research has supported this by demonstrating that in the early years, attributions and self-concept are more unidimensional but become more distinct and stable overtime (Muenks & Miele, 2017; Nicholls, 1978, 1979). Such general developmental effects suggest, at minimum, a reciprocal causal relationship between attributions and skill, with past research supporting this contention (Skaalvik & Valås, 1999; Wigfield & Karpathian, 1991). However, other studies have provided evidence for directionality (Marsh, Byrne, & Yeung, 1999). Although attributions are shaped by past performance, those same attributions and related expectancies may impact later performance. For instance, although Chapman and Tunmer (1997) found that earlier experiences with reading were related to students’ developing self-concept, Marsh (1990, 1999) found that prior self-concept was associated with later achievement (see also Wigfield & Karpathian, 1991). Despite these developmental trends, a recent cross-sectional study has shown that correlations between attributions of success and/or failure to ability persist independent of age and vary dynamically across levels of ability (Tsujimoto et al., 2018).

Person-centered approach to reading motivation

Many existing studies on reading motivation have relied on variable-oriented methodologies, focusing on relationships between motivation constructs and achievement. An exception is Marsh, Lüdtke, Trautwein, and Morin (2009), who used latent profile analysis (LPA) to explore dimensions of academic self-concept. The objective of LPA is to identify unique and unobserved groups of similar individuals who can be compared to individuals in different distinct groupings (Lubke & Muthén, 2005; Williams & Kibowski, 2016). The flexibility for model specification with LPA gives this approach an advantage over related cluster analyses. An additional advantage is the ability to compare models, resulting in an informed decision regarding the best model fit (Marsh et al., 2009). LPA was implemented in the current study because it allows for a person-centered approach to identifying patterns across multiple attribution dimensions.

The current study

The present study sought to investigate patterns of reading-related causal attributions, with an overall goal to establish whether profiles are linked with attention and reading skills. Although previous research has shown support for the contextual and domain-specificity of reading attributions (Tsujimoto et al., 2018), we anticipated that a portion of our sample would have co-occurring difficulties in both reading and attention and that the combination of even mild impairments may be associated with less adaptive attributions. This work also considered participant demographics with a racially and ethnically diverse sample. Reading motivation research has predominantly included White/European American samples, leaving racially/ethnically diverse samples understudied (Cox & Yang, 2012; Guthrie, Coddington, & Wigfield, 2009). Graham (1994) indicated that attribution styles do not significantly differ between those of African American and White/European American learners. This review highlighted that motivation studies with a focus on race contributions had primarily been comparative and future studies should investigate motivation-achievement relationships using within-race/ethnicity designs.

The study was guided by three main objectives: (a) to investigate patterns of reading-related attributions; (b) to examine attribution profiles as a function of varying levels of reading skills, inattention, and hyperactivity; and (c) to examine how age, race/ethnicity, sex, and socioeconomic status (SES) may (or may not) contribute to reading-related attributions. We anticipated identifying unique patterns of attributions that ranged from more to less adaptive. We expected that children with greater achievement (or attention skills) would show more adaptive attribution patterns with positive perceptions of ability (e.g., “I am a good reader”) and overall greater attributions to controllable attributes (e.g., “I could have read the story more carefully”). Conversely, a reverse, less adaptive pattern was expected among lower achieving children.

Methods

Sample

The sample consisted of 1,315 participants 8–15 years of age (Mage = 11.09, SD = 2.13). Missing data were minimal (2.21%) and Little’s Missing Completely at Random test (Little, 1988) provided evidence that data were missing completely at random, η2(38) = 52.25, p = .06. The expectation maximization algorithm was used to complete the data set using single imputation. Participants were predominantly African American and Hispanic/Latino racial/ethnic heritage. Community, school, and clinic-based enrollment methods were used across seven sites from the United States, Canada, and Puerto Rico. To ensure representation across the developmental spectrum of reading ability, oversampling of the lower end of this distribution was employed. Additional participants were recruited who had a history of poor reading (as documented by school or clinical testing), report of skills falling below expected age or grade level, and/or provision of special services in reading. Descriptive statistics and correlations among study variables are provided in Tables 1 and ​2.

Table 1.

Descriptive Statistics Including Demographics and Reading Scores

Table 2.

Correlations Among Study Variables

Participants were excluded if they met any of the following criteria: nonminority ethnic or racial group membership, foster care placement, preterm birth (defined as < 36 weeks gestation), more than 5 days in the neonatal intensive care unit after birth, history of diagnosed or suspected significant cognitive delays, significant behavioral problems or frequent school absences, serious emotional/psychiatric disturbances (i.e., major depression, psychosis, or pervasive developmental disorder/autism), chronic neurologic conditions, or documented vision impairment or hearing loss.

Details of participation were reviewed by parents, who gave informed consent; likewise, the study was fully described to children, who provided their own free and informed assent for participation. Following completion of the test battery, participants were given a $35 gift card. Ethical review and oversight was provided by the Yale Human Investigations Committee, with additional review and oversight from the university Institutional Review Boards of associated data collection sites in Boston, Colorado, Maryland, and Toronto. Each of these institutions conform to U.S. Federal Policy for the Protection of Human Subjects, except the Hospital for Sick Children in Toronto, Canada, which conforms to the Canadian national Tri-Council Policy Statement for the Ethical Conduct of Research with Human Participants.

Measures

Attributions

The Sydney Attribution Scale (SAS; Marsh, 1984) is a well-validated self-report measure of causal attributions. Participants were presented with 13 scenarios of reading-related activities that had an outcome of either success (e.g., “Suppose you really did well on a reading test”) or failure (e.g., “Suppose your teacher says you are doing badly in reading work”). Three statements representing perceived causes were paired with each scenario: one for ability (e.g., “Your reading is good”/“Your reading is poor”), another for effort (e.g., “You work hard at reading”/“You should have read it more carefully”), and one for external causes (e.g., “You were lucky”/“The teacher made a mistake”). Statements were rated on a Likert-type scale with five degrees of endorsement. When necessary, items were read to participants (e.g., children younger than 10 years old, those with reading difficulties who struggled with earlier assessments).

Reliability analysis using the greatest lower bound from the psych library (Revelle, 2014) was used, as it provides an estimate that neither underestimates nor overestimates reliability for samples greater than 1,000 (Ten Berge & Sočan, 2004). This estimate is interpreted on the same scale as other reliability coefficients (Revelle & Zinbarg, 2009; Sijtsma, 2009). The greatest lower bound for six subscales were as follows: Success to Ability = .90; SUCCESS TO EFfort = .82; Success to External = .76; Failure to Ability = .83; Failure to Effort = .71; Failure to External = .59. All subscales, except Failure to External Causes, demonstrated acceptable to good reliabilities. The two External subscales were excluded due to poor model fit (i.e., low entropy); therefore, the identification of profiles was based on internal attributions to ability and effort only.

Reading skill

For word-level reading, the Sight Word Efficiency (SWE) and Phonemic Decoding Efficiency (PDE) subtests from the Test of Word Reading Efficiency (TOWRE; Torgesen, Wagner, & Rashotte, 1999) were used. Participants were given 45 s to read as many real words (SWE) or pseudowords (PDE) correctly as possible, gradually increasing in difficulty. Alternate-form reliability exceeds .88 for SWE and .91 for PDE.

The Peabody Picture Vocabulary Test-Third Edition (Dunn & Dunn, 2007) was included as a measure of receptive vocabulary. After a word is presented, children were asked to select a picture that best represents the word meaning. Test-retest reliability exceeds .91 within the age range of this sample.

The Passage Comprehension subtest from the Standardized Reading Inventory-Second Edition (Newcomer, 1999) was used. Children were presented with 10 passages of increasing difficulty. A contextual reading accuracy score was calculated by subtracting errors from the total number of words in a passage. Following the passage, children answer a series of comprehension questions. Internal consistency scores for the age of this sample were acceptable, ranging from .88 to .97.

Inattention and hyperactivity

One parent rated inattention and hyperactivity symptoms of ADHD using the Strengths and Weakness of Attention-Deficit/Hyperactivity Disorder Symptoms and Normal Behavior Scale (SWAN; Swanson et al., 2006). A total score for each subscale was used with greater values indicating greater levels of inattention and hyperactivity/impulsivity.

Demographics

Race, ethnicity, and SES were gathered from the primary caregiver via interview and/or questionnaire. A composite variable for SES distinguished between participants who took part in government assistance programs (e.g., food stamps; Women, Infants, and Children; Medicaid; and/or any other related public/government assistance) and those who did not.

Analysis plan

An LPA was conducted using Mplus (version 8.0; Muthén & Muthén, 1998–2017) to identify subgroups based on reading-related attributions. Indicators included four SAS subscales (i.e., Success to Ability, Success to Effort, Failure to Ability, and Failure to Effort). Raw scores on TOWRE SWE and PDE subtests, total SWAN scores for inattention and hyperactivity, and age were entered simultaneously as continuous covariates. Direct inclusion of covariates is thought to influence only the probabilities for each class rather than distribution across classes and can improve the accuracy of classifications and parameter coverage (Lubke & Muthén, 2005).

We sought to investigate the implications of varying levels of reading and attention, including co-occurring impairments in both, in the context of reading-related attributions. Our analytic approach wanted to ensure the inclusion of relevant convergent predictors when further examining profile probabilities and therefore included, vocabulary, word-level and comprehension as indicators of reading ability given that word-level versus high-order reading-related skills may be differentially associated with attributions. Finally, as a test of divergent validity we used chi-square tests of independence to confirm latent classes were not associated with external demographics.

Results

Latent profile analysis

The use of different sets of starting values is recommended for mixture models as multiple local maxima likelihood are expected (Hipp & Bauer, 2006; Muthén et al., 2002). We increased the number of random starting values to 1,000 with a recommended number of 20 iterations (Muthén & Muthén, 1998–2017). To further reduce the risk of multiple local maxima, we doubled the number of random starts. The best log-likelihood value was replicated and the structure of the solution remained identical. To ensure that, at minimum, level differences were controlled across sites, the LPA was formulated as a multilevel model, with children nested within data collection sites. Although the distribution of participants across sites and the number of sites was insufficient to make substantive interpretations at this level, and Level 2 was not of substantive interest, this strategy introduced some control for the classification and estimates at Level 1. The multilevel formulation also resulted in exclusion of three cases from the final model, making a final total sample of 1,312.

Results of model fit testing two through five groups are shown in Table 3. Fit criteria included log likelihood, Akaike information criterion, Bayesian information criterion, entropy, and the Lo-Mendell-Rubin Adjusted Likelihood Ratio Test. Lower values for log likelihood, Akaike information criterion, and Bayesian information criterion are associated with better fit. However, greater entropy values suggest greater classification power. A significant Lo-Mendell-Rubin Adjusted Likelihood Ratio Test indicates that model fit has improved in comparison to the model with one less class. Although this test was not significant for the three-group and four-group models, the interpretation of profiles and other fit indices improved with a four-group model.

Table 3.

Indices for LPA Models With Two, Three, Four, and Five Groups

Attribution profiles

Mean scores of ability and effort attributions for reading success and failure across groups are listed in Table 4 and illustrated in Figure 1. For reading success, Group 1 (n = 528) and Group 4 (n = 159) demonstrated the greatest mean scores for ability attributions (i.e., “I am a good reader”) and equivalent scores for effort attributions (i.e., “I work hard at reading”). These groups differed in attributions for reading failure. Group 1 showed the lowest mean score for attributing failure to lack of ability and had low effort attributions. In comparison, Group 4 had the greatest mean score for attributing effort to reading failure and had a moderate mean score for attributing reading failure to ability.

Latent profile analysis plot for attribution groups. Note. The y-axis represents the 5-point scale for the Sydney Attribution Scale ranging 1 (false), 2 (mostly false), 3 (sometimes false/sometimes true), 4 (mostly true), and 5 (true). Greater scores indicate greater endorsement of each attribution type in response to the behavioral vignette. Error bars represent 95% confidence intervals.

Table 4.

Mean Scores for Reading Achievement Attributions Across Four LPA Groups

Group 3 (n = 495) had attribution scores falling close to the scale midpoint or lower. However, this group generally had greater attributions to effort, compared to ability. Finally, Group 2 (n = 130) was the only group to manifest a markedly different and reversed pattern of attributions. The greatest mean scores for this group were first for attributions to ability for reading failure (e.g., “You are a poor reader”), followed by attributions to effort for reading success (e.g., “You tried very hard”). For reading success, this group also had the lowest attributions to ability (e.g., “You are a good reader”).

Reading as a function of latent profiles.

The probability of Group 1 classification increased with greater reading skill. For Group 3, the reverse was found, with lower reading skills associated with higher probability of group membership. A similar but weaker trend was found for the other groups. Figure 2 illustrates phonemic decoding as a function of attribution profiles and was replicated across both TOWRE measures.

Covariate plot for Test of Word Reading Efficiency (TOWRE) phonemic decoding efficiency as a function of attribution latent profiles. Note. Decoding skills were not associated with membership in either Group 2 or Group 4, the two profiles associated with higher attributions to ability in situations of failure and generally high attributions to effort. Higher decoding skills were strongly associated with membership in Group 1, the most adaptive ability attribution group. Lower decoding skills were associated with membership in Group 3, the group associated with the less distinct attribution pattern with success and failure attributions more similar to each other in level.

Regression parameterizations showed that Group 1 scores on PDE were significantly better compared to all other groups. However, only the strongest (Group 1) and weakest (Group 2) reading groups differed on SWE (B = −.04, SE = .02, p < .05). Group 4 had the second greatest mean scores on both reading measures, but this group was only significantly stronger than the weakest performing readers in Group 2 for SWE (B = −.03, SE = .01, p < .01).

No differences in comprehension were observed, and groups did not differ in receptive vocabulary, apart from the strongest and weakest reading skill groups, with the former being superior. Mean reading scores and regression coefficients comparing attribution profiles are presented in Table 5.

Table 5.

Z Tests for Pairwise Comparisons of TOWRE Scores Across LPA Attribution Groups

ADHD behaviors as a function of latent profiles.

Good attention was associated with Group 1. The reverse pattern was observed for Group 3 with increasing classification as inattention increased (see Figure 3). This pattern was replicated for hyperactivity/impulsivity, but as the pairwise comparisons of latent means below suggests, the pattern was much weaker.

Covariate plot for Strengths and Weakness of Attention-Deficit/Hyperactivity Disorder Symptoms and Normal Behavior Scale (SWAN) Inattention as a function of latent profiles. Note. Inattention was not associated with membership in either Group 2 or Group 4, the two profiles associated with higher attributions to ability in situations of failure and generally high attributions to effort. Lack of inattention was moderately associated with membership in Group 1, the most adaptive ability attribution group. Inattention was associated with membership in Group 3, the group associated with the less distinct attribution pattern, with success and failure attributions more similar to each other in level.

Inattention was lowest for Group 1 members who were significantly different from Group 2 (B = .05, SE = .02, p < .01) and Group 3 (B = .03, SE = .01, p < .001). Group 2 showed the highest levels of inattention, though no differences were observed compared to Group 3 (B = −.02, SE = .01, p = .19). In addition, Group 2 showed significantly more hyperactivity/impulsivity in comparison to Group 1 (B = .02, SE = .01, p < .01). Mean inattention and hyperactivity/impulsivity scores and regression coefficients comparing attribution profiles are presented in Table 6.

Table 6.

Pairwise Comparisons of SWAN Scores Across LPA Attribution Groups

There were also significant age differences among profiles, F(3, 1311) = 27.83, p < .001. Group 1 members were younger than those in other groups. Group 2 was also significantly younger than the adaptive effort Group 4 but slightly older than Group 3 (see Table 7). Overall, decreasing age was strongly associated with Group 1 membership but weakly and positively associated with membership in other groups.

Table 7.

Regression Coefficients Comparing Age Among LPA Groups

Divergent validation

Covariates were chosen to examine varying levels of reading skills and attention in relation to attributions and each demonstrated an association with attribution profiles. Evidence that this model is meaningful and specific to the interrelationships between reading attributions and reading/attention factors could be found in a test of divergent validity. Group membership was evaluated against these variables outside the core model. Chi-square analyses assessed whether attribution groups were differentially represented by race/ethnicity, sex, and SES. Results revealed that neither Hispanic ethnicity, η2(3) = 7.28, p = .06; nor African American race, η2(3) = 5.09, p = .17; nor SES, η2(3) = 2.22, p = .53, was related to differences in attribution profiles. However, differing proportions of male and female participants were found, η2(3) = 9.40, p = .02. Three of the four groups had fewer female participants (43.4% to 44.6%); however, Group 1 had more female participants (52.8%).

Model identification: Cross-tabulations

Two additional LPA models were run and compared against each other. The first model grouped individuals based on attributions alone. The second model grouped individuals based on reading and ADHD behaviors. This method replicated the four aforementioned attribution profiles. The second model identified four groups of learners ranging from poor to better reading and attention.

Significant differences between groups in the attribution only model versus the reading and attention model,η2(9) = 53.692, p < .001, were found. Group labels are consistent with those previously described. Almost half of the learners in Group 1 (about 47%) were classified with very good reading and low inattention and hyperactivity/impulsivity. Approximately 21% of Group 2 were classified with weaker reading and higher inattention. Approximately 79% of the learners in Group 3 (midpoint) were classified with either moderate reading and attention scores (about 40%) or slightly lower reading and moderate attention scores (about 39%). Finally, learners in Group 4 were split. Approximately 31% were classified with either weak reading and high inattention (about 15%) or very good reading and low ADHD traits (about 16%).

Discussion

Defining groups with LPA

For models including covariates, class membership is model specific rather than to an individual (Lubke & Muthén, 2005). Moreover, interpretations of latent classes may be altered when model specifications are modified (Anderson & Gerbing, 1988; Howell, Breivik, & Wilcox, 2007; Marsh et al., 2009). The person-centered approach of LPA has advantages when investigating reading motivation given that attributions are contextual and closely associated with specific experiences. The objective was to develop an understanding of patterns in attributions while considering their association with reading skills, inattention, and hyperactivity. Even a moderate number of symptoms associated with ADHD introduces challenges with reading (Martinussen, 2015), which may in turn impact reading-related attributions. We opted not to assign specific profile labels but instead characterized attribution patterns as more or less adaptive while offering empirical and theoretical context for the discussion.

Identifying patterns in reading-related attributions

Adaptive reading attributions

Results indicated two distinct groups with positive ability attributions (Group 1 and Group 4). Members of these groups were the most likely to attribute reading success to ability and had the greatest attributions to effort in situations of success, suggesting a sense of control over positive learning outcomes (Weiner, 1985, 2010). Group 1 also had the lowest attributions to lack of ability in response to reading failure. This arguably “adaptive ability” group had scores below the midpoint for attributions to reading failure. The term positivity bias has been used to explain a tendency to have greater accountability for successful events with greater attributions to internal and stable causes in comparison to negative events (Marsh, 1986; Mezulis, Abramson, Hyde, & Hankin, 2004). This may explain generally stronger attributions to ability in the event of reading success relative to failure, especially among learners confident in their skills or protective of their ability perceptions.

Group 4 had a similar pattern but differed with greater attributions to effort for reading failure. Although this group had slightly greater attributions to lack of ability in response to failure, we argue that this group showed the most adaptive pattern of attributions given that they had the overall greatest attributions to controllable causes (Perry & Hamm, 2017). Although the difference in effort attributions in situations of success is minimal, most interesting is the difference for reading failure. This adaptive effort group had scores more than a full point higher on the SAS Effort subscale in comparison to the adaptive ability group. Effort attributions are particularly meaningful in situations of failure, as attributions to internal and controllable causes are suggestive of adaptiveness to challenges. Group 3 showed a similar pattern with greater attributions to effort compared to ability. However, with mean scores falling near the midpoint, and with less discrepancy between ability and effort attributions, this group was considered moderately adaptive.

Less adaptive reading attributions

Group 2 presented the least adaptive pattern of attributions. Members were characterized by the greatest association of reading failure with lack of ability and were the least likely to associate reading success with being a good reader. This group had the greatest discrepancy between ability attributions comparing reading success and failure. They also had the greatest discrepancy between associating success to effort rather than ability. Muenks and Miele (2017) asserted that viewing ability and effort as inversely related translates into the perception that greater effort is required with low ability.

Attribution profiles and relations to reading skills and attention

Typically performing learners vs. those with difficulties

More adaptive patterns have been reported for typically performing students in comparison to those with learning difficulties. Similar to Group 1, typically performing learners are more likely to attribute positive events like success to more stable and internal causes (i.e., ability; Hamm, Perry, Clifton, Chipperfield, & Boese, 2014; Margolis & MaCabe, 2006). Our results also suggested an adaptive effort attribution profile. This pattern of greater attributions to controllable causes has also been reported for typically performing learners (e.g., Tabassam & Grainger, 2002). Covariate analyses supported this by showing the greatest reading and lowest inattention scores for learners with these adaptive attribution profiles. The probability of adaptive profile membership grew as reading scores increased and inattention decreased.

Furthermore, members with the least adaptive attributions had lower reading skills and greater inattention with increasing membership probability as reading scores decreased. Covariate plots suggested that membership was weakly associated with inattention. However, with the greatest inattention scores, this group significantly differed from the two most adaptive groups. Together these results complement previous literature indicating more positive attributions for typically achieving learners. Those who struggle tend to exhibit less adaptive attributions (e.g., Chapman, 1988; Morgan, Fuchs, Compton, Cordray, & Fuchs, 2008; Núñez et al., 2005; Wolters, Deton, York, & Francis, 2014) and have a greater tendency to associate failure with internal stable causes, rather than controllable ones. Individuals with co-occurring reading and attention difficulties may be especially at risk due to greater learning impairments (Hoza et al., 2001; Lee & Zentall, 2012; Tabassam & Grainger, 2002; Zentall & Beike, 2012). Results suggest that even a moderate number of reading and/or attention difficulties are associated with a similar risk.

Higher order reading skills such as comprehension did not contribute to significant group differences among attribution profiles. The same was found for vocabulary, except for one effect between profiles with the greatest and poorest reading skills. Given that we sampled the full range of reading ability, with oversampling at the lower range, the link between attributions and measures of decoding is quite relevant. Differences in reading-related attributions maybe most evident among more extreme groups of typically performing readers and those with more profound reading difficulties.

Midpoint attribution profile

Although the pattern of attributions were similar to the most adaptive group, weaker endorsement and discrepancy between ability and effort attributions did not qualify this group as equally adaptive. Learners were significantly more inattentive and had poorer reading performance than the adaptive ability group but stronger than then least adaptive group. Overall, covariate analyses suggested increased membership probability as reading scores decreased and inattention increased. Together these trends suggest that this group represents learners with moderate challenges but not comparable to those with the least adaptive attributions. This may explain the trend toward an adaptive attribution profile despite the presence of some learning difficulties.

Attributions of similar performing groups

Results suggested that learners with reading and/or attention difficulties may have less adaptive attribution profiles. However, despite possible learning challenges, the midpoint group did not have maladaptive attributions. Although RD and ADHD populations tend to exhibit negative attribution styles and poor self-efficacy compared to typicall developing peers (Lee & Zentall, 2012; Tabassam & Grainger, 2002), there is some evidence that learners with ADHD may overestimate academic competency (Owens, Goldfine, Evangelista, Hoza, & Kaiser, 2007). Few differences in hyperactivity/impulsivity may be explained by closer associations between inattentive subtypes of ADHD and academic underachievement (Martinussen et al., 2014; Massetti et al., 2008). Inattentive learners struggle with learning components ranging from sustained attention during lessons (Hoza et al., 2001) to task initiation and completion (Lanberg et al., 2010). A self-serving bias may serve as a protective factor against learned helplessness. This overestimation may, however, be specific to competence motivation. Children with attention difficulties still tend to exhibit poorer self-concept and global self-worth (Frame, Kelly, & Bayley, 2003; Lee & Zentall, 2012; Wiener et al., 2012), as well as greater frustration and less persistence with academic tasks (Hoza et al., 2001).

Struggling readers typically demonstrate negative self-perceptions (Chan, 1994; Chapman, 1988; Lee & Zentall, 2012; Morgan et al., 2008; Núñez et al., 2005; Tabassam & Grainger, 2002; Wolters et al., 2014). However, Zeleke (2004) contended that, given the multidimensional nature of self-concept, the conclusion that all students with learning disabilities have more negative self-concept than typically developing peers is unjustified. Our results similarly suggest that some learners may be resilient to negative attribution styles.

The two adaptive groups had similar reading and attention, except for a significantly lower phonemic decoding efficiency score for the most adaptive group. This may explain greater effort attributions as a tendency to attribute success to effort can indicate a feeling of increased effort to be successful (Muenks & Miele, 2017). Relatedly, the chi-square analysis showed that a proportion of learners in the most adaptive group had some reading and attention difficulties. However, an equivalent proportion of learners in this group had typical reading and attention. Greater attributions to effort may also be associated with a learning or mastery goal orientation involving greater persistence (Dweck, 1986; Wigfield & Guthrie, 2000). In summary, it can be argued that within similar performing groups, there are differences in attributions that extend beyond reading skill or attention alone.

Divergent validation

The adaptive ability group was significantly younger than all other groups. Fewer experiences with failure among younger learners may explain more positive ability attributions, or a slower rate of developmental decline in positive perceptions of ability (Tsujimoto et al., 2018). Consistent with this, and past findings by Frijters et al. (2018), the oldest group had the most adaptive profile. This group performed significantly lower than the youngest “adaptive ability” group on a measure of phonemic decoding efficiency. Relatively greater experiences of difficulties over time may result in attributing success to effort, but also as children age they are better able to understand differences between ability and effort and how they may be reciprocally related (Nicholls, 1978, 1979). Although not the focus of the study, results demonstrated that age played a significant role as a covariate and warrants further study in the context of attributions.

Given a dominance of reading motivation studies with White/European-American participants (Cox & Yang, 2012; Gurthrie et al., 2009), a contribution of this work was the sampling focus on underrepresented populations. There were equal proportions of race/ethnicity and SES within each profile. In the context of a well-powered chi-square test, this suggests that achievement attributions are more closely associated with specific learning experiences and related abilities rather than demographics.

Implications for future research

This work presented promising findings that suggest the presence of unique reading-related attribution profiles. Results support past studies that have characterized typically achieving students as having more adaptive attribution profiles compared to those with learning challenges (e.g., Chapman, 1988; Lee & Zentall, 2012; Morgan et al., 2008; Núñez et al., 2005; Tabassam & Grainger, 2002; Wolters et al., 2014). However, findings also suggested that differences in attribution patterns can be identified within similar performing populations, as previously proposed by Núñez et al. (2005).

Although there are dynamic, and possibly developmental, links between attributions and reading/attention, the study design was insufficient to draw conclusions regarding directionality (i.e., do attributions drive achievement, or vice versa). Even without such a claim, understanding patterns of attributions can inform remedial efforts in reading. Fostering adaptive attributions may be especially important for struggling learners (Berkeley, Mastropieri, & Scruggs, 2011; Licht et al., 1985) who may exert less effort if they feel that success is unattainable (Hamm et al., 2014). Teaching realistic attributions to those who overestimate reading competency is equally important to ensure effort within the intervention context. Attribution retraining work has presented convincing evidence that remedial efforts with attention to motivation can assist achievement (Lazowski & Hulleman, 2016) by promoting adaptive reasoning for learning outcomes (Hamm et al., 2014; Haynes, Perry, Stupnisky, & Daniels, 2009; Perry, Chipperfield, Hladkyj, Pekrun, & Hamm, 2014; Toland & Boyle, 2008). The struggling reader who associates failures with internal, unstable, and controllable causes, such as effort, may develop greater academic self-concept and persistence that supports achievement (Berkeley et al., 2011; Lin, Coburn, & Eisenberg, 2016; Linnenbrink & Pintrich, 2003; Toland & Boyle, 2008).

The presence of adaptive and maladaptive/poor attribution styles is not disputed (Chodkiewicz & Boyle, 2014; Kistner et al., 1988; Linnenbrink & Pintrich, 2003; Núñez et al., 2005; Wolters, Fan, & Daugherty, 2013), but further studies on patterns of attributions, including models with new and replicated covariates, will assist the development of profile descriptors. Efforts were made to reduce the risk of local maxima; however, like other reading-focused implementations of LPA (Kornilov & Grigorenko, 2017; Ozernov-Palchik et al., 2016), we did not replicate our solution on an independent sample. A similarly large sample with this population does not, to our knowledge, exist. Finally, future studies should incorporate attributions to both internal and external causes. Poorer reliability for the External subscales may be related to varying types of external attributions (e.g., task difficulty vs. student-teacher relationships). Although initial LPA model formulations incorporated the external scales, this attribution dimension did not inform latent class patterns. This prevented strong conclusions about the contributions of this dimension; however, this should not be taken as evidence for its importance/unimportance. Understanding how children attribute reading outcomes to both internal and external sources would aid the understanding of individual differences in attributions.

Conclusion

Reading motivation is multifaceted, and attributions are arguably one of the more complex dimensions of motivation. Many questions remain regarding the influence of achievement attributions with future reading behaviors. The present study suggests that unique patterns of attributions are meaningfully related to reading skill and attention; however, attribution styles may change over time. A nuanced understanding of reading motivation has the potential to inform models of both reading development and disability.

Acknowledgments

This study and the larger Genes, Reading and Dyslexia (GRaD) study would not have been possible without the financial support of the Manton Family Foundation. Furthermore, additional support for JRG was provided by P50 HD 027802-22 and 2R01-NS43530- 08. There is no potential conflict of interest reported by any of the authors.

Funding

Supported by a grant from the Manton Foundation; National Institute of Child Health and Human Development [P50 HD 027802-22]; National Institute of Neurological Disorders and Stroke [2R01-NS43530-08].

Footnotes

Disclosure statement

No potential conflict of interest was reported by the authors.

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